View Source Scholar.Manifold.Trimap (Scholar v0.3.1)
TriMap: Large-scale Dimensionality Reduction Using Triplets.
TriMap is a dimensionality reduction method that uses triplet constraints to form a low-dimensional embedding of a set of points. The triplet constraints are of the form "point i is closer to point j than point k". The triplets are sampled from the high-dimensional representation of the points and a weighting scheme is used to reflect the importance of each triplet.
TriMap provides a significantly better global view of the data than the other dimensionality reduction methods such t-SNE, LargeVis.
The global structure includes relative distances of the clusters, multiple scales in the data, and the existence of possible outliers. We define a global score to quantify the quality of an embedding in reflecting the global structure of the data.
Based on: https://github.com/google-research/google-research/blob/master/trimap/trimap.py Source: https://arxiv.org/pdf/1910.00204.pdf
Summary
Functions
Embeds the given inputs using the TriMap algorithm.
Functions
Embeds the given inputs using the TriMap algorithm.
Options
:num_components(pos_integer/0) - Dimension of the embedded space. The default value is2.:num_inliers(pos_integer/0) - Number of inliners to sample. The default value is10.:num_outliers(pos_integer/0) - Number of outliers to sample. The default value is5.:num_random(pos_integer/0) - Number of random triplets to sample. The default value is3.:weight_temp(float/0) - Temperature for the tempered log. The default value is0.5.:learning_rate(float/0) - Learning rate. The default value is0.1.:num_iters(pos_integer/0) - Number of iterations. The default value is400.:init_embedding_type- Method to initialize the embedding. 0 for PCA, 1 for random. The default value is0.:key- Random key used in Trimap. If the key is not provided, it is set toNx.Random.key(System.system_time()).:triplets(term/0) - Triplets to use in the Trimap algorithm.:weights(term/0) - Weights to use in the Trimap algorithm.:init_embeddings(term/0) - Initial embeddings to use in the Trimap algorithm.:metric- Metric used to compute the distances. The default value is:squared_euclidean.:knn_algorithm- Algorithm used to compute the nearest neighbors. Possible values::nndescent- Nearest Neighbors Descent. SeeScholar.Neighbors.NNDescentfor more details.:large_vis- LargeVis algorithm. SeeScholar.Neighbors.LargeVisfor more details.:brute- Brute force algorithm. SeeScholar.Neighbors.BruteKNNfor more details.:auto- Automatically selects the algorithm based on the number of points.
The default value is
:auto.
Examples
iex> {inputs, key} = Nx.Random.uniform(Nx.Random.key(42), shape: {30, 5})
iex> Scholar.Manifold.Trimap.transform(inputs, num_components: 2, num_inliers: 3, num_outliers: 1, key: key, knn_algorithm: :nndescent)